Specify the tissue of interest, run the boilerplate code which sets up the functions and environment, load the tissue object.

tissue_of_interest = "Aorta"
library(here)
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
tiss <- load_tissue_facs(tissue_of_interest)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Scaling data matrix"

  |                                                                                                                             
  |                                                                                                                       |   0%
  |                                                                                                                             
  |=======================================================================================================================| 100%
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

PCElbowPlot(object = tiss)

# Set number of principal components. 
n.pcs = 10
# Set resolution 
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.Clustering parameters for resolution 0.5 exactly match those of already computed. 
  To force recalculation, set force.recalc to TRUE.
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)

# Batch and animal effects
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")

TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.id")

Check expression of genes of interset.

Dotplots let you see the intensity of expression and the fraction of cells expressing for each of your genes of interest.

How big are the clusters?

table(tiss@ident)

  0   1   2   3   4   5 
127  84  54  47  35  17

Which markers identify a specific cluster?

clust.markers <- FindMarkers(object = tiss, ident.1 = 3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~06s          
   |+                                                 | 2 % ~06s          
   |++                                                | 3 % ~06s          
   |++                                                | 4 % ~06s          
   |+++                                               | 5 % ~06s          
   |+++                                               | 6 % ~06s          
   |++++                                              | 7 % ~06s          
   |++++                                              | 8 % ~06s          
   |+++++                                             | 9 % ~06s          
   |+++++                                             | 10% ~05s          
   |++++++                                            | 11% ~05s          
   |++++++                                            | 12% ~05s          
   |+++++++                                           | 13% ~05s          
   |+++++++                                           | 14% ~05s          
   |++++++++                                          | 15% ~05s          
   |++++++++                                          | 16% ~05s          
   |+++++++++                                         | 17% ~05s          
   |+++++++++                                         | 18% ~05s          
   |++++++++++                                        | 19% ~05s          
   |++++++++++                                        | 20% ~05s          
   |+++++++++++                                       | 21% ~05s          
   |+++++++++++                                       | 22% ~05s          
   |++++++++++++                                      | 23% ~05s          
   |++++++++++++                                      | 24% ~05s          
   |+++++++++++++                                     | 25% ~05s          
   |+++++++++++++                                     | 26% ~05s          
   |++++++++++++++                                    | 27% ~04s          
   |++++++++++++++                                    | 28% ~04s          
   |+++++++++++++++                                   | 29% ~04s          
   |+++++++++++++++                                   | 30% ~04s          
   |++++++++++++++++                                  | 31% ~04s          
   |++++++++++++++++                                  | 32% ~04s          
   |+++++++++++++++++                                 | 33% ~04s          
   |+++++++++++++++++                                | 34% ~04s          
   |++++++++++++++++++                                | 35% ~04s          
   |++++++++++++++++++                                | 36% ~04s          
   |+++++++++++++++++++                               | 37% ~04s          
   |+++++++++++++++++++                               | 38% ~04s          
   |++++++++++++++++++++                              | 39% ~04s          
   |++++++++++++++++++++                              | 40% ~04s          
   |+++++++++++++++++++++                             | 41% ~04s          
   |+++++++++++++++++++++                             | 42% ~04s          
   |++++++++++++++++++++++                            | 43% ~03s          
   |++++++++++++++++++++++                            | 44% ~03s          
   |+++++++++++++++++++++++                           | 45% ~03s          
   |+++++++++++++++++++++++                           | 46% ~03s          
   |++++++++++++++++++++++++                          | 47% ~03s          
   |++++++++++++++++++++++++                          | 48% ~03s          
   |+++++++++++++++++++++++++                         | 49% ~03s          
   |+++++++++++++++++++++++++                         | 50% ~03s          
   |++++++++++++++++++++++++++                        | 51% ~03s          
   |++++++++++++++++++++++++++                        | 52% ~03s          
   |+++++++++++++++++++++++++++                       | 53% ~03s          
   |+++++++++++++++++++++++++++                       | 54% ~03s          
   |++++++++++++++++++++++++++++                      | 55% ~03s          
   |++++++++++++++++++++++++++++                     | 56% ~03s          
   |+++++++++++++++++++++++++++++                     | 57% ~03s          
   |+++++++++++++++++++++++++++++                     | 58% ~03s          
   |++++++++++++++++++++++++++++++                    | 59% ~02s          
   |++++++++++++++++++++++++++++++                    | 60% ~02s          
   |+++++++++++++++++++++++++++++++                   | 61% ~02s          
   |+++++++++++++++++++++++++++++++                   | 62% ~02s          
   |++++++++++++++++++++++++++++++++                  | 63% ~02s          
   |++++++++++++++++++++++++++++++++                  | 64% ~02s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~02s          
   |++++++++++++++++++++++++++++++++++                | 67% ~02s          
   |++++++++++++++++++++++++++++++++++               | 68% ~02s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~02s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06s
print(x = head(x= clust.markers, n = 10))

Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
Error in `[<-.data.frame`(`*tmp*`, , save.name, value = c("4", "0", "0",  : 
  replacement has 364 rows, data has 842
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='free_annotation')
Error in FetchData(object = object, vars.all = group.by) : 
  Error: free_annotation not found

Save the Robject for later

When you save the annotated tissue, please give it a name.

Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

Figures for Supplement

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